Publication Type:

Conference Paper

Source:

ICMLC 2010 - The 2nd International Conference on Machine Learning and Computing, Bangalore, p.121-124 (2010)

ISBN:

9780769539775

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-77953047001&partnerID=40&md5=ddfc50b9e6b07b54ac9f17315a67aded

Keywords:

After-images, Artificial Neural Network, Backpropagation, Backpropagation network, Compression ratios, Digital image storage, Image compressing, Image compression, Learning systems, Mean square error, Neural networks, Peak signal-to-noise ratio, Signal to noise ratio

Abstract:

This paper explores the application of artificial neural networks to image compression. An image compressing algorithm based on Back Propagation (BP) network is developed after image pre-processing. By implementing the proposed scheme the influence of different transfer functions and compression ratios within the scheme is investigated. It has been demonstrated through several experiments that peak-signal-to-noise ratio (PSNR) almost remains same for all compression ratios while mean square error (MSE) varies. © 2010 IEEE.

Notes:

cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@17ab9730 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@252dc74 Through org.apache.xalan.xsltc.dom.DOMAdapter@e0744dd; Conference Code:80504

Cite this Research Publication

P. Va Rao, Madhusudana, Sb, Nachiketh, S. Sb, and Keerthi, Kc, “Image compression using artificial neural networks”, in ICMLC 2010 - The 2nd International Conference on Machine Learning and Computing, Bangalore, 2010, pp. 121-124.

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